In robust optimization problems, stochastic input variables are required. Based on this concept, \(x\)  and \(y\)  must be set. In this study, we impose a normal distribution with a standard deviation of 0.1 for the variables. It means that extra investigating points following the distribution are generated around each robust design. Latin hypercube sampling (LHS) is selected as the sampling method and it generates 50 points around the robust design. In order to find a stable solution, we set the following objectives for the function: (1) maximizing the mean value and (2) obtaining the lowest standard deviation (less than 0.1). The optimization process is implemented using multi-objective simulated annealing (MOSA) [1]}, [2]}, [3]} and 600 evaluations are performed.
